| Autor: |
Liu Ting-Long |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 30867-30875 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3364810 |
| Popis: |
For a long-term complex Action, it is typically composed of various short-term Actions. The speed and importance of these short-term Actions directly affect the recognition results. Current two-stream neural networks have already achieved good recognition results on Action recognition datasets. However, previous two-stream networks have focused more on Action modeling, neglecting the impact of the speed and importance of different short-term Actions on the results of Action recognition. This has directly limited the model’s ability to model different short-term Actions, thereby affecting the effectiveness of Action recognition. To address this issue, this paper proposes a Short-term Action Spatio-Temporal Attention (STASTA) module based on the two-stream network structure. The STASTA module is capable of focusing on the differences in importance and speed between different short-term Actions. By extracting the differences in importance and speed of different short-term Actions in the video and then fusing the features, the aim is to enrich spatio-temporal features and improve Action recognition performance. The proposed method is evaluated on the Something-Something v1 & v2 and Charades datasets. A large number of experimental results indicate that the method proposed in this paper achieves state-of-the-arts results among video Action recognition methods. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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